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A Video-Based Automated Recommender (VAR) System for Garments

Author

Listed:
  • Shasha Lu

    (Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom)

  • Li Xiao

    (School of Management, Fudan University, Shanghai 200433, China)

  • Min Ding

    (Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802; and School of Management, Fudan University, Shanghai 200433, China)

Abstract

In this paper, we propose an automated and scalable garment recommender system using real-time in-store videos that can improve the experiences of garment shoppers and increase product sales. The video-based automated recommender (VAR) system is based on observations that garment shoppers tend to try on garments and evaluate themselves in front of store mirrors. Combining state-of-the-art computer vision techniques with marketing models of consumer preferences, the system automatically identifies shoppers’ preferences based on their reactions and uses that information to make meaningful personalized recommendations. First, the system uses a camera to capture a shopper’s behavior in front of the mirror to make inferences about her preferences based on her facial expressions and the part of the garment she is examining at each time point. Second, the system identifies shoppers with preferences similar to the focal customer from a database of shoppers whose preferences, purchasing, and/or consideration decisions are known. Finally, recommendations are made to the focal customer based on the preferences, purchasing, and/or consideration decisions of these like-minded shoppers. Each of the three steps can be implemented with several variations, and a retailing chain can choose the specific configuration that best serves its purpose. In this paper, we present an empirical test that compares one specific type of VAR system implementation against two alternative, nonautomated personal recommender systems: self-explicated conjoint (SEC) and self-evaluation after try-on (SET). The results show that VAR consistently outperforms SEC and SET. A second empirical study demonstrates the feasibility of VAR in real-time applications. Participants in the second study enjoyed the VAR experience, and almost all of them tried on the recommended garments. VAR should prove to be a valuable tool for both garment retailers and shoppers.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.0984 .

Suggested Citation

  • Shasha Lu & Li Xiao & Min Ding, 2016. "A Video-Based Automated Recommender (VAR) System for Garments," Marketing Science, INFORMS, vol. 35(3), pages 484-510, May.
    • Handle: RePEc:inm:ormksc:v:35:y:2016:i:3:p:484-510
    DOI: 10.1287/mksc.2016.0984
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    16. Dellaert, B.G.C. & Baker, T. & Johnson, E.J., 2017. "Partitioning Sorted Sets: Overcoming Choice Overload while Maintaining Decision Quality," ERIM Report Series Research in Management 18-2, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
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